I disagree.

I think it is absolutely immoral (and dumb from an IP risk point of view) for companies to try to get interviewees to solve real problems for free.

This is different though: this is about potential candidates finding creative ways to demonstrate their skills.

The companies didn't even ask people to do this: these people chose to take on projects that would demonstrate their skills in a new space in which which they did no yet have commercial experience. I think that's commendable and a very smart strategy.

This isn't different though. This article was written as "How to get into ML" and is basically selling the idea that getting into ML requires (or is aided by) doing some independent open source work to bulk up a profile.

A very competent developer who is interested in doing some ML work may have just recently read this article and gotten to work on an ML project because he feels that not doing so will hurt his chances of getting a job.

As a note, I have no idea what the right solution to this problem is, it is good to confirm candidate's knowledge (I prefer creative ways similar to the parent) and part of that knowledge certainly can include open source work, but going down this road too much leads to people feeling obligated to make open source work to put on their resume and even to people faking open source work to try and land a job.

"but going down this road too much leads to people feeling obligated to make open source work to put on their resume"

...which makes the world a better place. Why do you object to people writing open source code?

i agree, and I recommend doing it for those wishing to get into the field. i believe one of the things that helped me get where i am now is my performance in kaggle competitions.

i do have one concern if it ever became an industry prereq, it becomes a filter for those who have more free time to work on side projects.

The current problem with things like data science thought pieces and MOOCs is that they don't prepare for the realities of machine learning/data science in the real world. (longer blog post on the subject: https://minimaxir.com/2018/10/data-science-protips/)

Doing a unique project is much better for learning cost/benefits of implementing AI/ML, although this post may be overoptimistic on how that can lead to a job offer.

Thanks for posting the article. While I've read up on ML, I don't work in the industry. I'm curious to see what other people think of the article. Can other people weigh in?

I agree with my sibling comment and would add:

Max is correct to point out the irony in his anti-thoughtpiece thoughpiece as he falls into the same trap of vagueness as those other articles. Specifically, he rails against general “black box” approaches to modeling, then takes a general “black box” approach to the work of operationalizing a model (much harder than building the prototype to begin with!).

The discussion of “pulling data” does not match the practical reality, since pulling via BI tools is not scalable and rarely automatable. SQL may cover this insofar as you dump data from SQL to...what, though? A Python session on your laptop? Automating this process allows a data scientist to scale their impact.

For more specificity on engineering practices required for data science, I recommend Robert Chang’s series of posts: https://link.medium.com/CG7c7mQdyS

For details on how a data scientist can impact an organization, I recommend this from the FirstMark blog by Jeremy Stanley and Daniel Tunkelang: https://firstround.com/review/doing-data-science-right-your-...

For clarity, I only covered the data science part of the perspective; the data engineering/DevOps part is another, more difficult can of worms which would require its own post!

I'm a junior SWE at a mid-sized AI startup. The article is pretty much spot on, but I'm not sure that the romantic view of data science the article argues against is actually that prevalent.

It seems like you would need a very specific level of knowledge to romanticize data science in that way. Most people know too little (So you're basically trying to build skynet?), and most of the rest are either in the industry or know someone that is, and so have a more realistic view.

I don't know, I did maths in undergrad, maybe some of the more clueless CS majors thought this way.

The reality is that there are enough people with experience in machine learning that companies don't need to train people.

This is not remotely true. It's true that there are lots of data scientists who can string together sklearn or R models. However, very few people know how these models work, when to apply them _and why_ or what concerns need to be addressed on data, metrics and deployment. Even fewer know how to improve these models once they catch all of the low-hanging fruit.

Playing Devil's advocate: The other side is they hire a false positive, and suffer through the different sad state of affairs?

Skunkworks: omg this kid has replicated our Auto-GAS system using only computer vision

Lockheed Martin CEO: So what the fuck is this youtube video?

https://www.youtube.com/watch?v=WkZGL7RQBVw